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Simultaneous use of different scalarizing functions in MOEA/D

Authors:

Hisao Ishibuchi,

Noritaka Tsukamoto,

Yusuke NojimaAuthors Info & Claims

GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation

Pages 519 - 526

https://doi.org/10.1145/1830483.1830577

Published: 07 July 2010 Publication History

Abstract

The use of Pareto dominance for fitness evaluation has been the mainstream in evolutionary multiobjective optimization for the last two decades. Recently, it has been pointed out in some studies that Pareto dominance-based algorithms do not always work well on multiobjective problems with many objectives. Scalarizing function-based fitness evaluation is a promising alternative to Pareto dominance especially for the case of many objectives. A representative scalarizing function-based algorithm is MOEA/D (multiobjective evolutionary algorithm based on decomposition) of Zhang & Li (2007). Its high search ability has already been shown for various problems. One important implementation issue of MOEA/D is a choice of a scalarizing function because its search ability strongly depends on this choice. It is, however, not easy to choose an appropriate scalarizing function for each multiobjective problem. In this paper, we propose an idea of using different types of scalarizing functions simultaneously. For example, both the weighted Tchebycheff (Chebyshev) and the weighted sum are used for fitness evaluation. We examine two methods for implementing our idea. One is to use multiple grids of weight vectors and the other is to assign a different scalarizing function alternately to each weight vector in a single grid.

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Index Terms

Simultaneous use of different scalarizing functions in MOEA/D
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction

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cover image ACM Conferences

GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation

July 2010

1520 pages

ISBN:9781450300728

DOI:10.1145/1830483

General Chair:
Martin Pelikan
University of Missouri, USA
,
Program Chair:
Jürgen Branke
University of Warwick, Coventry, UK

Copyright © 2010 ACM.

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Published: 07 July 2010

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GECCO '10: Genetic and Evolutionary Computation Conference

July 7 - 11, 2010

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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https://doi.org/10.3390/rs16050854
Chu XHan XLi S(2024)Tracking Moving Targets by Earth Observation Satellites: A Multi-Objective Scheduling Approach2024 International Conference on New Trends in Computational Intelligence (NTCI)10.1109/NTCI64025.2024.10776156(37-41)Online publication date: 18-Oct-2024
https://doi.org/10.1109/NTCI64025.2024.10776156
Omran SEl-Behaidy WYoussif A(2023)Optimization of Cryptocurrency Algorithmic Trading Strategies Using the Decomposition ApproachBig Data and Cognitive Computing10.3390/bdcc70401747:4(174)Online publication date: 14-Nov-2023
https://doi.org/10.3390/bdcc7040174
Su YLin QMing ZTan K(2023)Adapting Decomposed Directions for Evolutionary Multiobjective OptimizationIEEE Transactions on Cybernetics10.1109/TCYB.2022.316555753:10(6289-6302)Online publication date: Oct-2023
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